MAGI-ACMG: Algorithm for the Classification of Variants According to ACMG and ACGS Recommendations

We have developed MAGI-ACMG, a classification algorithm that allows the classification of sequencing variants (single nucleotide or small indels) according to the recommendations of the American College of Medical Genetics (ACMG) and the Association for Clinical Genomic Science (ACGS). The MAGI-ACMG classification algorithm uses information retrieved through the VarSome Application Programming Interface (API), integrates the AutoPVS1 tool in order to evaluate more precisely the attribution of the PVS1 criterion, and performs the customized assignment of specific criteria. In addition, we propose a sub-classification scheme for variants of uncertain significance (VUS) according to their proximity either towards the “likely pathogenic” or “likely benign” classes. We also conceived a pathogenicity potential criterion (P_POT) as a proxy for segregation criteria that might be added to a VUS after posterior testing, thus allowing it to upgrade its clinical significance in a diagnostic reporting setting. Finally, we have developed a user-friendly web application based on the MAGI-ACMG algorithm, available to geneticists for variant interpretation.


Introduction
Classification of germline variants from genetic testing of patients with rare disorders has significantly evolved in the last decade.In the original American College of Medical Genetics guidelines (ACMG) [1], definitions of criteria for variant interpretation were quite broad, but in the following years ad hoc specifications were released for many of them to provide in depth guidance on their assignment (e.g., PVS1 [2], PS3/BS3 [3], PP3/BP4 [4]).
In addition, gene-or disease-specific guidelines have been developed by expert panels, to evaluate accurately gene-specific features, disease prevalence, and inheritance patterns, or characteristic disease-causing mechanisms [5,6].
Although there has been a substantial effort to develop standardized guidelines and protocols in the field of variant interpretation, each of the aforementioned tools employs different definitions and cutoffs to assign the classification criteria, relying on internal calibration of thresholds, database accessibility and users' contribution.Therefore, the interpretation of the same variant may vary using different tools, often shifting between the three intermediate classes, likely benign/uncertain significance/likely pathogenic.Moreover, many databases used to retrieve variant statistics and information are constantly updated, and new functional evidence and case reports become available in the literature.In addition, many laboratories are specialized in the analysis of particular macro-areas of rare diseases, and therefore internal databases represent valuable sources of knowledge acquired through the analysis of several affected individuals, revealing, for instance, sub-population specific causative variants.Thus, as knowledge evolves, re-evaluation of dubious variants in time is encouraged, as the initial interpretation might need modifications.
We have previously described the integration of the VarSome Application Programming Interface (API) into the NGS data analysis workflow of our molecular genetics' laboratory [10], followed by the development of an interactive ACMG-based classifier that allows us to interpret single nucleotide variants (SNVs) or small deletions/insertions (indels) from NGS-based testing, using the information retrieved through the VarSome environment [7] and which performs variant classification according to the recommended ACMG combinatory rules, with some internally defined modifications [11].
According to the ACMG recommendations [1], the Association for Clinical Genomic Science (ACGS) suggested that a sub-classification system for variants of unknown significance (VUS) might be useful for laboratories to decide which of these should be reported, according to the different levels of evidence supporting their pathogenicity, and according to the likelihood that further data might allow a reclassification of variants as likely pathogenic or pathogenic [12].Indeed, retrospective testing, such as appropriate familiar segregation analysis, might be useful to demonstrate the de novo occurrence of a VUS in a gene associated with a dominantly inherited disorder (PS2/PM6 criteria), or the cosegregation of a VUS in multiple affected individuals separated by a significant number of meioses (PP1 criterion), or the in-trans occurrence of the VUS with a likely pathogenic/pathogenic variant in the same gene for disorders inherited in a recessive manner (PM3 criterion).
In this work, we propose a subclassification scheme for VUS, to automatize the selection of which variants should be reported in the diagnostic setting according to their different proximity either to the likely pathogenic/pathogenic classes or to the likely benign/benign ones.Moreover, taking into consideration that further segregation analysis might allow us to add specific criteria to the VUS and upgrade its classification, a pathogenicity potential criterion (P_POT) is automatically added under specific circumstances to highlight VUS that should be included in the final diagnostic report.
In the developed interpretative algorithm, the attribution of some ACMG criteria is customized by comparison with VarSome implementation, while others are kept as attributed by VarSome [11].We have also developed MAGI-ACMG, a web application that offers geneticists a variant interpretation tool that applies the described framework and can be accessed through a web browser (http://magiacmg.magiclinici.it:8805,accessed on 1 August 2023).

MAGI-ACMG Algorithm Description
The development of an automated tool for the classification of sequence variants according to the original combinatorial ACMG rules, the integration of VarSome API in our diagnostic pipeline and the customization of some ACMG criteria have been described before [10,11].Briefly, all variants with a decision MAF (minor allele frequency) below 3%-calculated by integrating frequencies from dbNSFP, VEP and gnomAD-are submitted for annotation through the VarSome Stable-API environment [11].The attribution of a number of criteria is customized through the MAGI-ACMG algorithm, and is therefore independent of the VarSome specifications.The final classification is reached through the combinatorial scheme proposed by the ACMG guidelines 2015.The MAGI-ACMG algorithm performs the following Strength modifications to: The MAGI-ACMG algorithm independently evaluates the following criteria: • PVS1: this criterion is assigned using the AutoPVS1 tool [13], which is based upon the detailed ClinGen guidelines for the application of the PVS1 criteria [2].REVEL and CADD optimal cutoffs were retrieved from the recent ClinGen calibration study for computational predictors [4].The REVEL score cutoffs (0.644 and 0.29) were applied also to the other predictors rank scores retrieved from dbNSFP v.4 [14].AdaBoost and RF score cutoffs were retrieved from [15].

Subclassification of Variants of Uncertain Significance
According to the recommendations of ACGS [12], we have subclassified VUS into three categories, according to their different proximity to the upper (likely pathogenic and pathogenic) or lower (likely benign and benign) classes: Hot, Middle and Cold (Table 1).In the diagnostic setting, it is important to report only VUS that have a high chance of being re-evaluated as likely pathogenic following posterior testing, such as familial segregation analysis that might reveal: (1) the de novo occurrence of a VUS in a gene associated with a dominantly inherited disorder; (2) co-segregation of a VUS in a significant number of affected family members (PP1); and (3) genes associated with recessive disorders, the in-trans configuration of a VUS with a pathogenic/likely pathogenic variant in the same gene (PM3).
In order to select which variants should be reported in the diagnostic setting, we introduced a "pathogenicity potential" criterion (P_POT), which is assigned by the algorithm to Middle VUS in the following conditions, at a Moderate level:

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If in the same individual another pathogenic/likely pathogenic/Hot/Middle VUS is identified in a gene associated with an autosomal recessive disorder; The P_POT criterion is downgraded to a Supporting level if the Middle VUS has the following criteria combinations: In these conditions, Middle VUS receiving a P_POT criterion become potentially Hot and can be included in the clinical report.When familial segregation is already available (contextual trio analysis or parallel analysis of multiple affected individuals), the P_POT can be inactivated and substituted by the corresponding pathogenicity criterion, according to the different situations described above.Contextual analysis might also demonstrate that the pathogenicity criteria are not applicable, and the opposite benignity criteria can be applied instead (BS4, BP2, BS2).Therefore, in the diagnostic setting both Cold and Middle VUS are not reported.

Implementation of MAGI ACMG Web Application
The front end of the web application is built using Vue.Js.In this part, the user uploads the variants either manually in the "Input DATA" text area or by uploading a .csvfile using the format SAMPLEID, PANEL_NAME, ANNOTATION.The SAMPLEID and PANEL can be any string of text, while the ANNOTATION should be GENE_NAME:NM#:c.123A>T(e.g., CELSR1:NM_014246.4:c.5165G>A).After the CALCULATE button is pressed, the analysis begins, and upon completion, the list of uploaded variants is displayed in a lateral section.By selecting the queried variants, the MAGI-ACMG checklist is displayed with the criteria automatically assigned.The user can interact with the checklist by turning on other appropriate criteria, and the final verdict is recalculated.A PDF file can also be generated containing variant information such as annotation, assigned criteria, interpretation, and a list of functional predictors scores (Figure 1).The front-end section is built using Vue.Js.In this part, the user uploads the variants to be analyzed.The user can interact with the checklist to modify criteria and recalculate the final interpretation.The user can generate a PDF for each sample containing information on the analyzed variants.(b) Every query to the server has to pass an authentication mechanism.In this part, the bioinformatics engine performs the analysis for the uploaded variants and the MAGI-ACMG algorithm runs.(c) The back end is developed using Django REST Framework and PostgreSQL database.The data of the application is exposed as JSON via Django REST Framework application program interfaces (APIs).We have implemented serializers for converting data to execute requests and routing for API endpoints.
The ACMG MAGI web application is available at http://magiacmg.magiclinici.it:8805,accessed on 1 August 2023.The source code of the application can be found at the following link: https://gitlab.com/magieuregio/magi-acmg,accessed on 1 August 2023.

AutoPVS1 Implementation
For the variant NM_000372.5:c.1586del,NP_000363.1:p.(Leu529Tyrfs*7) in the TYR gene, the PVS1 criterion has been assigned at a Moderate level using the AutoPVS1 tool according to the specific guidelines [2].The PM2 criterion is always kept at a Moderate level (Table 3, Figure 3).

AutoPVS1 Implementation
For the variant NM_000372.5:c.1586del,NP_000363.1:p.(Leu529Tyrfs*7) in the TYR gene, the PVS1 criterion has been assigned at a Moderate level using the AutoPVS1 tool according to the specific guidelines [2].The PM2 criterion is always kept at a Moderate level (Table 3, Figure 3).

Figure 1 .
Figure 1.The general architecture of MAGI-ACMG web application.(a) The front-end section is built using Vue.Js.In this part, the user uploads the variants to be analyzed.The user can interact with the checklist to modify criteria and recalculate the final interpretation.The user can generate a PDF for each sample containing information on the analyzed variants.(b) Every query to the server has to pass an authentication mechanism.In this part, the bioinformatics engine performs the analysis for the uploaded variants and the MAGI-ACMG algorithm runs.(c) The back end is developed using Django REST Framework and PostgreSQL database.The data of the application is exposed as JSON via Django REST Framework application program interfaces (APIs).We have implemented serializers for converting data to execute requests and routing for API endpoints.

Figure 1 .
Figure 1.The general architecture of MAGI-ACMG web application.(a) The front-end section is built using Vue.Js.In this part, the user uploads the variants to be analyzed.The user can interact with the
is triggered at a Supporting or Strong level, this algorithm reassigns the standard Moderate intensity; • BP6: if the criterion is triggered at a Very Strong or Moderate level, the algorithm re-assigns a Supporting intensity.A subsequent strength confirmation is afterwards performed by the geneticist to confirm or upgrade the criteria to a Strong level.

Table 1 .
Subclassification of variants of uncertain significance (VUS) and combination of criteria that define them in each subcategory.In the diagnostic settings, Cold and Middle VUS are not

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If the middle VUS is in the homozygous state in a gene associated with autosomal recessive disorder; • If the middle VUS is in the heterozygous state in a gene associated with an autosomal dominant disorder; • If the middle VUS is the hemizygous state in a gene associated with an X-linked condition.